Federated Learning Over Wireless Edge Networks / / by Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao

• Record Nr.: UNINA9910616395503321
• Autore: Lim Wei Yang Bryan
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-07838-1
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (175 pages)
• Collana: Wireless Networks, , 2366-1445
• Disciplina: 929.374; 006.31
• Soggetti: Telecommunication; Computational intelligence; Machine learning; Artificial intelligence; Communications Engineering, Networks; Computational Intelligence; Machine Learning; Artificial Intelligence
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Federated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.
• Sommario/riassunto: This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the ; wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.